This dataset contains the total personal income tax and corporate tax collections and section-wise revenue foregone due to incentives, exemptions and deductions for all the categories of taxpayers viz., Corporate Sector; Non-Corporate Sector (Firms, Association of Persons, Body of Individuals etc.); and Individuals/ Hindu Undivided Families.

Note: 1. For Corporate sector, total revenue forgone is calculated by deducting the Net Additional Tax due to MAT. 2. Revenue Impact for 2023-24 are Projected.

The Personal Income Tax Rate in India stands at 39 percent. This dataset provides - India Personal Income Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

🧾 This is an original, manually created dataset of 250 Indian citizens, built to explore how people emotionally respond to India's taxation system.

It includes details like: - Monthly income (in INR) - Tax paid (in INR) - Profession type - Government benefits received - GST usage behavior - Awareness about billionaire taxation - Presence of billionaires in their area - And most importantly — their emotional response like:

“I feel exploited by the tax system.”

📌 Why this dataset matters: Unlike most datasets that only focus on numbers, this dataset brings in human perception and emotional impact. It bridges the gap between economics and public sentiment — opening up powerful use cases for data science, ML, and public policy modeling.

🎯 Ideal For: - EDA and storytelling through visuals - Social impact ML projects - Classification models (predict who feels exploited) - Government policy analysis - Real-world data exploration and feature engineering

🧠 Created with purpose: This dataset was manually curated and structured by Swasthik Poojari — as part of his open-source ML project on tax fairness and emotional inequality in India.

🔗 GitHub Project: TaxTruth: Income & Exploitation ML Analysis

Let data speak not just in numbers — but in emotion, voice, and fairness.

The dataset comprises of state-wise data on the number of persons who filed Income Tax returns as well as the state-wise data on the number of persons whose Income Tax returns amount to zero tax liability.

The Corporate Tax Rate in India stands at 34.94 percent. This dataset provides - India Corporate Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Dataset Description: Indian Urban Affordability and Economic Productivity (221 Cities) About the Dataset

This dataset represents the comprehensive 221-city version developed and utilized in the research paper “Predicting Urban Affordability and Economic Productivity in India: A Data-Driven KNN and Random Forest Framework with Insights from Selected Major Cities.”

It builds upon the author’s earlier 70-city affordability dataset and significantly expands its scope.

The dataset provides a unified framework to study how urban affordability, digital readiness, and GDP specialization jointly influence economic livability and productivity across different city tiers.

Data Provenance and Construction

Primary Source: Extended web-scraped affordability data originally compiled from LivingCost.org and other verified open-data platforms.

Cleaning & Standardization: City names normalized (e.g., “Bengaluru” → “Bangalore”), and all numeric fields standardized to INR using a consistent USD→INR conversion rate for comparability.

Features Included

Each record (row) corresponds to one city and contains the following metrics:

Cost of Living (INR)

Monthly Rent (INR)

Monthly After-Tax Salary (INR)

Income After Rent (INR)

Affordability Ratio (“Months Covered”)

Intended Applications

This dataset can be used for:

🧮 Cross-city affordability and livability analysis

🤖 Machine Learning model development (affordability or salary prediction)

🌆 Urban economics and policy simulation studies

📈 Correlation and regression-based research in ICT and GDP domains

📊 Dashboard and visualization projects (Power BI, Tableau, SAP SAC, etc.)

It is designed for use by researchers, policymakers, educators, and data analysts seeking a reliable, structured, and multi-domain dataset on Indian urban dynamics.

Data Quality and Transparency

✅ Uniform currency and value scaling

✅ Reproducible preprocessing (Python-based pipelines with Scikit-Learn)

✅ Missing values imputed using KNN-based methodology

✅ Verified against baseline datasets used in prior research

✅ Released under Creative Commons Attribution 4.0 International (CC BY 4.0) license

Significance

This dataset forms the empirical backbone of the author’s second research paper, providing the quantitative base for the KNN baseline model and the Random Forest multi-output regressor used to predict salary and affordability across Indian cities.

It enables city-level insight generation for policymakers and supports reproducible, data-driven research in urban economics, digital inclusion, and sustainable development.

Future Extensions

An upcoming enhancement will include:

Complete AQI integration for all 221 cities to examine the affordability–environment linkage.

Time-series extension for multi-year trend analysis.

Inclusion of healthcare, safety, and green infrastructure indicators for a broader livability framework.

A additional file used in my paper on T30 cities of India with justification is also attached.

This dataset contains a collection of personal financial and identification documents for individuals in India. The dataset includes images of a variety of financial documents such as bank statements, salary slips, income tax returns, utility bills and checks. If you have any questions or feedback regarding this dataset, please feel free to contact me.

The Sales Tax Rate in India stands at 18 percent. This dataset provides - India Sales Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Urbanisation is a form of social transformation from traditional rural societies to modern, industrial and urban communities. It is long term continuous process. It is progressive concentration of population in urban unit. Kingsley Davies has explained urbanisation as process of switch from spread out pattern of human settlements to one of concentration in urban centers. Migration is the key process underlying growth of urbanization.

Challenges in urban development--->;

Institutional challenges

Urban Governance 74th amendment act has been implemented half-heartedly by the states, which has not fully empowered the Urban local bodies (ULBs). ULBs comprise of municipal corporations, municipalities and nagar panchayats, which are to be supported by state governments to manage the urban development. For this , ULBs need clear delegation of functions, financial resources and autonomy. At present urban governance needs improvement for urban development, which can be done by enhancing technology, administrative and managerial capacity of ULBs.

Planning Planning is mainly centralized and till now the state planning boards and commissions have not come out with any specific planning strategies an depend on Planning commission for it. This is expected to change in present government, as planning commission has been abolished and now focus is on empowering the states and strengthening the federal structure.

In fact for big cities the plans have become outdated and do not reflect the concern of urban local dwellers, this needs to be take care by Metropolitan planning committee as per provisions of 74th amendment act. Now the planning needs to be decentralized and participatory to accommodate the needs of the urban dwellers.

Also there is lack of human resource for undertaking planning on full scale. State planning departments and national planning institutions lack qualified planning professional. Need is to expand the scope of planners from physical to integrated planning- Land use, infrastructure, environmental sustainability, social inclusion, risk reduction, economic productivity and financial diversity.

Finances Major challenge is of revenue generation with the ULBs. This problem can be analyzed form two perspectives. First, the states have not given enough autonomy to ULBs to generate revenues and Second in some case the ULBs have failed to utilize even those tax and fee powers that they have been vested with.

There are two sources of municipal revenue i.e. municipal own revenue and assigned revenue. Municipal own revenue are generated by municipal own revenue through taxes and fee levied by them. Assigned revenues are those which are assigned to local governments by higher tier of government.

There is growing trend of declining ratio of own revenue. There is poor collection property taxes. Use of geographical information system to map all the properties in a city can have a huge impact on the assessment rate of properties that are not in tax net.

There is need to broaden the user charge fee for water supply, sewerage and garbage disposal. Since these are the goods which have a private characteristics and no public spill over, so charging user fee will be feasible and will improve the revenue of ULBs , along with periodic revision. Once the own revenue generating capacity of the cities will improve, they can easily get loans from the banks. At present due to lack of revenue generation capabilities, banks don’t give loan to ULBs for further development. For financing urban projects, Municipal bonds are also famous, which work on the concept of pooled financing.

Regulator

There is exponential increase in the real estate, encroaching the agricultural lands. Also the rates are very high, which are not affordable and other irregularities are also in practice. For this, we need regulator, which can make level playing field and will be instrumental for affordable housing and checking corrupt practices in Real estate sector.

Infrastructural challenges

Housing Housing provision for the growing urban population will be the biggest challenge before the government. The growing cost of houses comparison to the income of the urban middle class, has made it impossible for majority of lower income groups and are residing in congested accommodation and many of those are devoid of proper ventilation, lighting, water supply, sewage system, etc. For instance in Delhi, the current estimate is of a shortage of 5,00,000 dwelling units the coming decades. The United Nations Centre for Human Settlements (UNCHS) introduced the concept of “Housing Poverty” which includes “Individuals and households who lack safe, secure and healthy shelter, with basic infrastructure such as piped water and adequate provision for sanitation, drainage and the removal of hou...

This dataset contains 10,000 simulated sales transaction records, each represented in natural language with diverse sentence structures. It is designed to mimic how different users might describe the same type of transaction in varying ways, making it ideal for Natural Language Processing (NLP) tasks, text-based data extraction, and accounting automation projects.

Each record in the dataset includes the following fields:

Sale Date: The date on which the transaction took place. Customer Name: A randomly generated customer name. Product: The type of product purchased. Quantity: The quantity of the product purchased. Unit Price: The price per unit of the product. Total Amount: The total price for the purchased products. Tax Rate: The percentage of tax applied to the transaction. Payment Method: The method by which the payment was made (e.g., Credit Card, Debit Card, UPI, etc.). Sentence: A natural language description of the sales transaction. The sentence structure is varied to simulate different ways people describe the same type of sales event.

Use Cases: NLP Training: This dataset is suitable for training models to extract structured information (e.g., date, customer, amount) from natural language descriptions of sales transactions. Accounting Automation: The dataset can be used to build or test systems that automate posting of sales transactions based on unstructured text input. Text Data Preprocessing: It provides a good resource for developing methods to preprocess and standardize varying formats of text descriptions. Chatbot Training: This dataset can help train chatbots or virtual assistants that handle accounting or customer inquiries by understanding different ways of expressing the same transaction details.

Key Features: High Variability: Sentences are structured in numerous ways to simulate natural human language variations. Randomized Data: Names, dates, products, quantities, prices, and payment methods are randomized, ensuring no duplication. Multi-Field Information: Each record contains key sales information essential for accounting and business use cases.

Potential Applications: Use for Named Entity Recognition (NER) tasks. Apply for information extraction challenges. Create pattern recognition models to understand different sentence structures. Test rule-based systems or machine learning models for sales data entry and accounting automation.

License: Ensure that the dataset is appropriately licensed according to your intended use. For general public and research purposes, choose a CC0: Public Domain license, unless specific restrictions apply.

Description:

Explore the dynamic landscape of the Indian stock market with this extensive dataset featuring 4456 companies listed on both the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). Gain insights into each company's financial performance, quarterly and yearly profit and loss statements, balance sheets, cash flow data, and essential financial ratios. Dive deep into the intricacies of shareholding patterns, tracking the movements of promoters, foreign and domestic institutional investors, and the public.

This dataset is a rich resource for financial analysts, investors, and data enthusiasts. Perform thorough company evaluations, sector-wise comparisons, and predictive modeling. With figures presented in crore rupees, leverage the dataset for in-depth exploratory data analysis, time series forecasting, and machine learning applications. Stay tuned for updates as we enrich this dataset for a deeper understanding of the Indian stock market landscape. Unlock the potential of data-driven decision-making with this comprehensive repository of financial information.

Folder Structure:

• 4492 NSE & BSE Companies
  • Main directory containing data for 4456 NSE and BSE registered companies.
  • Company_name folder
    • Individual folders for each company allowing for easy organization and retrieval.
    • Company_name.csv
      • General company information.
    • Quarterly_Profit_Loss.csv
      • Quarterly financial data.
    • Yearly_Profit_Loss.csv
      • Annual financial data.
    • Yearly_Balance_Sheet.csv
      • Annual balance sheet information.
    • Yearly_Cash_flow.csv
      • Annual cash flow data.
    • Ratios.csv.csv
      • Financial ratios over time.
    • Quarterly_Shareholding_Pattern.csv
      • Quarterly shareholding pattern.
    • Yearly_Shareholding_Pattern.csv
      • Annual shareholding pattern.

File Explanation:

Company_name.csv

- `Company_name`: Name of the company.
- `Sector`: Industry sector of the company.
- `BSE`: Bombay Stock Exchange code.
- `NSE`: National Stock Exchange code.
- `Market Cap`: Market capitalization of the company.
- `Current Price`: Current stock price.
- `High/Low`: Highest and lowest stock prices.
- `Stock P/E`: Price to earnings ratio.
- `Book Value`: Book value per share.
- `Dividend Yield`: Dividend yield percentage.
- `ROCE`: Return on capital employed percentage.
- `ROE`: Return on equity percentage.
- `Face Value`: Face value of the stock.
- `Price to Sales`: Price to sales ratio.
- `Sales growth (1, 3, 5, 7, 10 years)`: Sales growth percentage over different time periods.
- `Profit growth (1, 3, 5, 7, 10 years)`: Profit growth percentage over different time periods.
- `EPS`: Earnings per share.
- `EPS last year`: Earnings per share in the last year.
- `Debt (1, 3, 5, 7, 10 years)`: Debt of the company over different time periods.

Quarterly_Profit_Loss.csv

 - `Sales`: Revenue generated by the company.
 - `Expenses`: Total expenses incurred.
 - `Operating Profit`: Profit from core operations.
 - `OPM %`: Operating Profit Margin percentage.
 - `Other Income`: Additional income sources.
 - `Interest`: Interest paid.
 - `Depreciation`: Depreciation of assets.
 - `Profit before tax`: Profit before tax.
 - `Tax %`: Tax percentage.
 - `Net Profit`: Net profit after tax.
 - `EPS in Rs`: Earnings per share.

Yearly_Profit_Loss.csv

- Same as Quarterly_Profit_Loss.csv, but on a yearly basis.

Yearly_Balance_Sheet.csv

- `Equity Capital`: Capital raised through equity.
- `Reserves`: Company's retained earnings.
- `Borrowings`: Company's borrowings.
- `Other Liabilities`: Other financial obligations.
- `Total Liabilities`: Sum of all liabilities.
- `Fixed Assets`: Company's long-term assets.
- `CWIP`: Capital Work in Progress.
- `Investments`: Company's investments.
- `Other Assets`: Other non-current assets.
- `Total Assets`: Sum of all assets.

Yearly_Cash_flow.csv

- `Cash from Operating Activity`: Cash generated from core business operations.
- `Cash from Investing Activity`: Cash from investments.
- `Cash from Financing Activity`: Cash from financing (borrowing, stock issuance, etc.).
- `Net Cash Flow`: Overall net cash flow.

Ratios.csv.csv

- `Debtor Days`: Number of days it takes to collect receivables.
- `Inventory Days`: Number of days inventory is held.
- `Days Payable`: Number of days a company takes to pay its bills.
- `Cash Conversion Cycle`: Time taken to convert sales into cash.
- `Wor...

Income-Before-Tax Time Series for Angel One Limited. Angel One Limited provides broking and advisory services, margin funding, and financial products to its clients in India and internationally. The company provides equity, commodities, derivatives, and currency derivative products. It also manages client's securities in the electronic form; facilitates clients in applying for initial public offerings; and provides funds to investors for trading. In addition, the company offers insurance, mutual funds, sovereign gold bonds, and credit products; and investment advice and market research services, as well as educates clients on financial markets and investing strategies. Further, it provides portfolio management, trading account, initial public offering, and DEMAT account services. The company operates through Angel One Super App, Angel One Trade, and Smart API investing and trading platforms. It serves resident and non-resident individuals, salaried professionals, high net worth individuals, Hindu undivided Families, corporates and trusts, partnership firms and LLP, and co-operative societies. The company was formerly known as Angel Broking Limited and changed its name to Angel One Limited in September 2021. Angel One Limited was incorporated in 1996 and is based in Mumbai, India.

Income-Before-Tax Time Series for PNB Housing Finance Limited. PNB Housing Finance Limited operates as a housing finance company in India. It provides loans to individuals and corporate bodies for purchase, construction, repair, and up-gradation of houses/flats/commercial properties, etc.; residential plot loans and loans for NRIs; loan against property, lease rental discounting, and loans for real estate developers; and home loans and fixed deposit products. The company was incorporated in 1988 and is based in Gurugram, India.

Tax-Provision Time Series for Anand Rathi Wealth Limited. Anand Rathi Wealth Limited provides financial advisory, brokerage, and consultancy services in India. The company offers wealth solutions, including investment planning and risk management solutions. It also operates Omni Financial Advisor, a technology platform for mutual fund distributors and independent financial advisors, which include client reporting, business dashboard, client engagement, online mutual fund transactions, and goal planning products. It serves high net-worth individuals and ultra-high net-worth individuals. The company was formerly known as Anand Rathi Wealth Services Limited and changed its name to Anand Rathi Wealth Limited in January 2021. Anand Rathi Wealth Limited was founded in 1994 and is based in Mumbai, India.

Tax-Provision Time Series for Bank of Baroda. Bank of Baroda Limited provides various banking products and services to individuals, government departments, and corporate customers in India and internationally. It operates through Treasury, Corporate/Wholesale Banking, Retail Banking, and Other Banking Operations segments. The company offers savings, salary, and current accounts; and fixed and recurring deposits. It also provides loans, including home, vehicle, personal, education, gold, mortgage, and other loans, as well as Baroda Yoddha loans for defense personnel and fintech credit; loans and advances for corporate, agriculture, and micro, small, and medium enterprises; export finance, foreign currency credits, foreign currency non-resident loans, external commercial borrowing, and import finance; and supply chain finance. In addition, the company offers life insurance, general insurance, and health insurance products; digital payment, instant banking, and merchant payment solutions; debit, credit, and prepaid cards; investment products; treasury; trade, FX, and remittances; and other services. Bank of Baroda Limited was incorporated in 1908 and is headquartered in Vadodara, India.

This dataset provides values for PERSONAL INCOME TAX RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides tax rates (Value Added Tax, Entry Tax, Luxury Tax) on tobacco products in 10 Indian States (Karnataka, Kerala, Goa, Madhya Pradesh, Gujarat, Haryana, Bihar, West Bengal, Meghalaya, and Nagaland) for the period of 1990-2017. The dataset provides tax rates for three major categories of tobacco products (cigarettes, bidis, and smokeless products) month-wise starting from the financial year April 1990 - March 1991 till the financial year April 2016 - March 2017. These data were collected from relevant statutes, notifications, public notices by concerned state governments typically available through state government commercial tax department websites occasionally supplemented by free internet searches for specific documents not available or accessible on state government websites.The following points will help better understand the dataset and its strengths and limitations:The numerical data in each cell refers to the rate of the tax on given tobacco product that prevailed at a given time (month/year). The data provided is a decimal fraction and is to be multiplied by 100 to derive the percentage e.g. 0.01 in the dataset imply 1% of tax rate.The Value Added Tax (VAT) Acts were enacted in Indian states in early 2000s and generally came to be implemented around the year 2005. In our dataset, we capture the VAT rates on tobacco from March 2005 onward. However, the actual implementation could have been a little earlier in some states. VAT rates are generally provided till March 2017 after which, VAT was subsumed in the Goods and Services Tax.In case of the Entry Tax and the Luxury Tax, only some of the states levied such taxes on tobacco products. In case of the states that levied these taxes on tobacco, we have captured data from March 1990 onward as our study period was 1990-2017. This does not necessarily imply that such taxes were not levied on tobacco before March 1990.Blank cells or cells with missing values denote that the given tax type was not levied on the given tobacco products for that time point.At times, additional tax or surcharge was levied under the VAT Act in addition to the VAT rate for tobacco. The dataset provides the VAT rates that are inclusive of such additional tax or surcharge and in such cases, a comment clarifying this has been inserted in the dataset.At times, different smokeless tobacco products had different tax rates levied on them. In such cases, we have generally indicated the highest tax rate in the dataset while including a comment clarifying the different rates for different smokeless tobacco items.Rarely, the VAT rate was levied in form of a fixed amount per certain number of products (cigarette sticks) instead of a fixed percentage of the product value. In such instance, we have inserted a comment in the dataset clarifying this.We found it complex to track all the changes done in tax rates on tobacco over time under these three tax categories. There were several amendments to the tax legislations and several notifications issued under these tax legislations regarding changes in tax rates on tobacco. It is likely that we missed out capturing all these changes, especially as some of the notifications were missing from the government websites. So, there are likely to be errors in terms of the tax rates and the exact period for which specific rates prevailed. We tried our best to capture data from authoritative sources as much as possible given the limited time and resources we had.This dataset was produced as part of the broader research project that explored the political economy of tobacco, titled “Deciphering an epidemic of epic proportion: the role of state and tobacco industry in tobacco control in post-liberalised India (1990-2017)”. We thank the DBT/Wellcome Trust India Alliance for funding this project through the Intermediate (Clinical and Public Health) Fellowship awarded to Upendra Bhojani (IA/CPHI/17/1/503346). While collecting these data, an earlier document compiling tax rates on tobacco at state level by Mr. Gaurav Gupta of the Campaign for Tobacco-Free Kids for the period 2010-2011 to 2016-2017 served as a useful reference. We thank him for sharing such resource with us.

The Social Security Rate in India stands at 24 percent. This dataset provides - India Social Security Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

BackgroundTaxing sugar-sweetened beverages (SSBs) has been proposed in high-income countries to reduce obesity and type 2 diabetes. We sought to estimate the potential health effects of such a fiscal strategy in the middle-income country of India, where there is heterogeneity in SSB consumption, patterns of substitution between SSBs and other beverages after tax increases, and vast differences in chronic disease risk within the population.Methods and FindingsUsing consumption and price variations data from a nationally representative survey of 100,855 Indian households, we first calculated how changes in SSB price alter per capita consumption of SSBs and substitution with other beverages. We then incorporated SSB sales trends, body mass index (BMI), and diabetes incidence data stratified by age, sex, income, and urban/rural residence into a validated microsimulation of caloric consumption, glycemic load, overweight/obesity prevalence, and type 2 diabetes incidence among Indian subpopulations facing a 20% SSB excise tax. The 20% SSB tax was anticipated to reduce overweight and obesity prevalence by 3.0% (95% CI 1.6%–5.9%) and type 2 diabetes incidence by 1.6% (95% CI 1.2%–1.9%) among various Indian subpopulations over the period 2014–2023, if SSB consumption continued to increase linearly in accordance with secular trends. However, acceleration in SSB consumption trends consistent with industry marketing models would be expected to increase the impact efficacy of taxation, averting 4.2% of prevalent overweight/obesity (95% CI 2.5–10.0%) and 2.5% (95% CI 1.0–2.8%) of incident type 2 diabetes from 2014–2023. Given current consumption and BMI distributions, our results suggest the largest relative effect would be expected among young rural men, refuting our a priori hypothesis that urban populations would be isolated beneficiaries of SSB taxation. Key limitations of this estimation approach include the assumption that consumer expenditure behavior from prior years, captured in price elasticities, will reflect future behavior among consumers, and potential underreporting of consumption in dietary recall data used to inform our calculations.ConclusionSustained SSB taxation at a high tax rate could mitigate rising obesity and type 2 diabetes in India among both urban and rural subpopulations.Please see later in the article for the Editors' Summary

Net-Income-From-Continuing-Operations Time Series for HDFC Bank Limited. HDFC Bank Limited engages in the provision of banking and financial services to individuals and businesses in India, Bahrain, Hong Kong, Singapore, and Dubai. The company operates in three segments: Treasury, Retail Banking, Wholesale Banking, and Other Banking Services. It accepts savings, salary, current, rural, public provident fund, pension, and demat accounts; fixed and recurring deposits; and safe deposit lockers, as well as offshore accounts and deposits, and overdrafts against fixed deposits. The company also provides personal, home, car, two-wheeler, business, doctor, educational, gold, consumer, and rural loans; loans against properties, securities, mutual funds, rental receivables, and assets; loans for professionals; government sponsored programs; and loans on credit card, as well as working capital and commercial/construction equipment finance, healthcare/medical equipment and commercial vehicle finance, dealer finance, and term loans. In addition, it offers credit, debit, prepaid, and forex cards; payment and collection, export, import, remittance, bank guarantee, letter of credit, trade, hedging, and merchant and cash management services; insurance and investment products. Further, the company provides short term finance, bill discounting, structured finance, export credit, loan repayment, and documents collection services; online and wholesale, mobile, and phone banking services; unified payment interface, immediate payment, national electronic funds transfer, and real time gross settlement services; and channel financing, vendor financing, reimbursement account, money market, derivatives, employee trusts, cash surplus corporates, tax payment, and bankers to rights/public issue services, as well as financial solutions for supply chain partners and agricultural customers. It operates branches and automated teller machines in various cities/towns. The company was incorporated in 1994 and is headquartered in Mumbai, India.